Brain tumour segmentation using a triplanar ensemble of U-Nets

TL;DR

This paper introduces a triplanar ensemble of U-Nets with a tumor core prediction module for improved brain tumor segmentation in MRI images, achieving competitive results on the BraTS challenge datasets.

Contribution

The paper presents a novel triplanar ensemble network architecture with an independent tumor core module for more accurate brain tumor segmentation.

Findings

Achieved Dice scores of 0.77 for ET and TC, and 0.89 for WT on BraTS validation dataset.

Ranked 10th in BraTS'20 challenge with top performance on unseen test data.

Performed comparably to top methods from BraTS'17-19.

Abstract

Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater variability even in manual segmentations. In this work, we propose a triplanar ensemble network, with an independent tumour core prediction module, for accurate segmentation of these tumours and their sub-regions. On evaluating our method on the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, for tumour sub-regions, we achieved a Dice similarity coefficient of 0.77 for both enhancing tumour (ET) and tumour core (TC). In the case of the whole tumour (WT) region, we achieved a Dice value of 0.89, which is on par with the top-ranking methods from BraTS'17-19. Our method achieved an evaluation score that was the equal 5th highest value (with our method ranking in 10th place) in the BraTS'20 challenge, with mean Dice values of 0.81, 0.89 and 0.84 on ET, WT and TC regions respectively on the BraTS'20 unseen test dataset.